Reconnaissance and Planning algorithm for constrained MDP

20 Sep 2019Shin-ichi MaedaHayato WatahikiShintarou OkadaMasanori Koyama

Practical reinforcement learning problems are often formulated as constrained Markov decision process (CMDP) problems, in which the agent has to maximize the expected return while satisfying a set of prescribed safety constraints. In this study, we propose a novel simulator-based method to approximately solve a CMDP problem without making any compromise on the safety constraints... (read more)

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